The world has long dreamed of robots, machines, and computers that are artificially intelligent. From Hal in Arthur C. Clarke's Space Odyssey series and Rosie the maid in the Jetsons cartoon series to the shipboard computer in the Star Trek series and R2D2 and C3PO in the Star Wars saga, we have been fascinated by machines that can inherently learn, understand, and think.
While this makes for very good entertainment and may hold aspirational goals for future generations of machines, the problems associated with artificial intelligence and building intelligent machines are very complex. For instance, no system exists today that can satisfactorily engage in an open dialog with humans over arbitrary text, much less a system that can independently “learn” from such interactions and explain justified answers to complex questions.
There has been progress in this space. Well-known systems like IBM's Watson enjoyed success on the TV game show Jeopardy and Apple's Siri has certainly made it easier to find music and locations on Apple products. But these systems merely apply massive data, large training sets, shallow linguistic techniques, and machine learning techniques to the task of automatic question answering. These systems lack deep understanding. More recent work on the Reading Comprehension task remains focused on shallow statistical approaches with narrow answer-based metrics rather than requirements for logical understanding and fluent explanation. Still today, however, no computer system can autonomously read, build, and communicate a logical understanding and explanation of even an arbitrary 2nd-grade text.
Accordingly, there is an ongoing need for smarter machines that can learn and understand.